Overview

Dataset statistics

Number of variables14
Number of observations801
Missing cells786
Missing cells (%)7.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory387.4 KiB
Average record size in memory495.3 B

Variable types

NUM7
CAT6
BOOL1

Warnings

id_ticket has a high cardinality: 635 distinct values High cardinality
id_usuario is highly correlated with unnamed:_0 and 1 other fieldsHigh correlation
unnamed:_0 is highly correlated with id_usuario and 1 other fieldsHigh correlation
Unnamed: 0 is highly correlated with unnamed:_0 and 1 other fieldsHigh correlation
edad has 160 (20.0%) missing values Missing
fila has 624 (77.9%) missing values Missing
id_ticket is uniformly distributed Uniform
unnamed:_0 has unique values Unique
id_usuario has unique values Unique
Unnamed: 0 has unique values Unique
nombre has unique values Unique
amigos has 557 (69.5%) zeros Zeros
parientes has 607 (75.8%) zeros Zeros

Reproduction

Analysis started2020-10-09 03:35:31.171007
Analysis finished2020-10-09 03:35:49.213065
Duration18.04 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

unnamed:_0
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct801
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean448.701623
Minimum0
Maximum890
Zeros1
Zeros (%)0.1%
Memory size12.5 KiB
2020-10-09T00:35:49.503317image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile46
Q1228
median451
Q3672
95-th percentile846
Maximum890
Range890
Interquartile range (IQR)444

Descriptive statistics

Standard deviation257.1922765
Coefficient of variation (CV)0.5731922136
Kurtosis-1.194007379
Mean448.701623
Median Absolute Deviation (MAD)222
Skewness-0.01927052489
Sum359410
Variance66147.86711
MonotocityNot monotonic
2020-10-09T00:35:49.811388image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
89010.1%
 
29710.1%
 
30910.1%
 
30810.1%
 
30710.1%
 
30410.1%
 
30310.1%
 
30210.1%
 
30110.1%
 
30010.1%
 
29910.1%
 
29810.1%
 
29610.1%
 
45010.1%
 
29510.1%
 
29410.1%
 
29310.1%
 
29210.1%
 
29110.1%
 
29010.1%
 
28910.1%
 
28810.1%
 
28710.1%
 
28610.1%
 
31010.1%
 
Other values (776)77696.9%
 
ValueCountFrequency (%) 
010.1%
 
110.1%
 
210.1%
 
310.1%
 
410.1%
 
510.1%
 
610.1%
 
810.1%
 
910.1%
 
1010.1%
 
ValueCountFrequency (%) 
89010.1%
 
88910.1%
 
88810.1%
 
88710.1%
 
88610.1%
 
88510.1%
 
88410.1%
 
88310.1%
 
88210.1%
 
88110.1%
 

id_usuario
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct801
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean449.701623
Minimum1
Maximum891
Zeros0
Zeros (%)0.0%
Memory size12.5 KiB
2020-10-09T00:35:50.138254image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile47
Q1229
median452
Q3673
95-th percentile847
Maximum891
Range890
Interquartile range (IQR)444

Descriptive statistics

Standard deviation257.1922765
Coefficient of variation (CV)0.571917608
Kurtosis-1.194007379
Mean449.701623
Median Absolute Deviation (MAD)222
Skewness-0.01927052489
Sum360211
Variance66147.86711
MonotocityNot monotonic
2020-10-09T00:35:50.433812image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
89110.1%
 
29810.1%
 
31010.1%
 
30910.1%
 
30810.1%
 
30510.1%
 
30410.1%
 
30310.1%
 
30210.1%
 
30110.1%
 
30010.1%
 
29910.1%
 
29710.1%
 
45110.1%
 
29610.1%
 
29510.1%
 
29410.1%
 
29310.1%
 
29210.1%
 
29110.1%
 
29010.1%
 
28910.1%
 
28810.1%
 
28710.1%
 
31110.1%
 
Other values (776)77696.9%
 
ValueCountFrequency (%) 
110.1%
 
210.1%
 
310.1%
 
410.1%
 
510.1%
 
610.1%
 
710.1%
 
910.1%
 
1010.1%
 
1110.1%
 
ValueCountFrequency (%) 
89110.1%
 
89010.1%
 
88910.1%
 
88810.1%
 
88710.1%
 
88610.1%
 
88510.1%
 
88410.1%
 
88310.1%
 
88210.1%
 

volveria
Boolean

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size12.5 KiB
0
494 
1
307 
ValueCountFrequency (%) 
049461.7%
 
130738.3%
 
2020-10-09T00:35:50.651378image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Unnamed: 0
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct801
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean448.701623
Minimum0
Maximum890
Zeros1
Zeros (%)0.1%
Memory size12.5 KiB
2020-10-09T00:35:50.871125image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile46
Q1228
median451
Q3672
95-th percentile846
Maximum890
Range890
Interquartile range (IQR)444

Descriptive statistics

Standard deviation257.1922765
Coefficient of variation (CV)0.5731922136
Kurtosis-1.194007379
Mean448.701623
Median Absolute Deviation (MAD)222
Skewness-0.01927052489
Sum359410
Variance66147.86711
MonotocityNot monotonic
2020-10-09T00:35:51.156509image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
89010.1%
 
29710.1%
 
30910.1%
 
30810.1%
 
30710.1%
 
30410.1%
 
30310.1%
 
30210.1%
 
30110.1%
 
30010.1%
 
29910.1%
 
29810.1%
 
29610.1%
 
45010.1%
 
29510.1%
 
29410.1%
 
29310.1%
 
29210.1%
 
29110.1%
 
29010.1%
 
28910.1%
 
28810.1%
 
28710.1%
 
28610.1%
 
31010.1%
 
Other values (776)77696.9%
 
ValueCountFrequency (%) 
010.1%
 
110.1%
 
210.1%
 
310.1%
 
410.1%
 
510.1%
 
610.1%
 
810.1%
 
910.1%
 
1010.1%
 
ValueCountFrequency (%) 
89010.1%
 
88910.1%
 
88810.1%
 
88710.1%
 
88610.1%
 
88510.1%
 
88410.1%
 
88310.1%
 
88210.1%
 
88110.1%
 

tipo_de_sala
Categorical

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size12.5 KiB
4d
447 
normal
187 
3d
167 
ValueCountFrequency (%) 
4d44755.8%
 
normal18723.3%
 
3d16720.8%
 
2020-10-09T00:35:51.452964image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-09T00:35:51.664661image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-09T00:35:51.834906image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length2
Mean length2.933832709
Min length2

Overview of Unicode Properties

Unique unicode characters9
Unique unicode categories2 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
d61426.1%
 
444719.0%
 
n1878.0%
 
o1878.0%
 
r1878.0%
 
m1878.0%
 
a1878.0%
 
l1878.0%
 
31677.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter173673.9%
 
Decimal Number61426.1%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
444772.8%
 
316727.2%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
d61435.4%
 
n18710.8%
 
o18710.8%
 
r18710.8%
 
m18710.8%
 
a18710.8%
 
l18710.8%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin173673.9%
 
Common61426.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
444772.8%
 
316727.2%
 

Most frequent Latin characters

ValueCountFrequency (%) 
d61435.4%
 
n18710.8%
 
o18710.8%
 
r18710.8%
 
m18710.8%
 
a18710.8%
 
l18710.8%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII2350100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
d61426.1%
 
444719.0%
 
n1878.0%
 
o1878.0%
 
r1878.0%
 
m1878.0%
 
a1878.0%
 
l1878.0%
 
31677.1%
 

nombre
Categorical

UNIQUE

Distinct801
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size12.5 KiB
Señor Urbano Saturnino
 
1
Señor Rolando Miguel
 
1
Señor Americo Anibal
 
1
Señorita Catalina Elvira
 
1
Señor Pascual Salomon
 
1
Other values (796)
796 
ValueCountFrequency (%) 
Señor Urbano Saturnino10.1%
 
Señor Rolando Miguel10.1%
 
Señor Americo Anibal10.1%
 
Señorita Catalina Elvira10.1%
 
Señor Pascual Salomon10.1%
 
Señor Eduardo Jacinto10.1%
 
Señorita Olga del Tránsito10.1%
 
Señora Irma Hipolita10.1%
 
Señora Angela Erminia10.1%
 
Señorita Elsa Prudencia10.1%
 
Señor Fortunato Saul10.1%
 
Señor Luciano Felix10.1%
 
Señor Hipolito Pastor10.1%
 
Señor Horacio Adan10.1%
 
Señor Jorge Fermin Pablo10.1%
 
Señor Osvaldo Ireneo10.1%
 
Señorita Salvadora Concepcion10.1%
 
Señor Pablo Olgo10.1%
 
Señor Edmundo Godofredo10.1%
 
Señor Pedro Felix10.1%
 
Señor Antonio Oscar10.1%
 
Señor Héctor Livio Eduardo10.1%
 
Señorita Juana Matilde10.1%
 
Señor Juan10.1%
 
Señor Hipolito Isidoro10.1%
 
Other values (776)77696.9%
 
2020-10-09T00:35:52.090497image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique801 ?
Unique (%)100.0%
2020-10-09T00:35:52.403505image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length34
Median length21
Mean length21.22097378
Min length9

Overview of Unicode Properties

Unique unicode characters58
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
o195311.5%
 
16769.9%
 
e16659.8%
 
r15208.9%
 
a15208.9%
 
i11907.0%
 
S8825.2%
 
ñ8014.7%
 
n7654.5%
 
l7054.1%
 
t5493.2%
 
s3952.3%
 
d3922.3%
 
u3151.9%
 
c2551.5%
 
A2301.4%
 
m2221.3%
 
E1661.0%
 
g1610.9%
 
b1260.7%
 
R1240.7%
 
J1140.7%
 
M1130.7%
 
C1060.6%
 
v890.5%
 
Other values (33)9645.7%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter1285775.6%
 
Uppercase Letter246514.5%
 
Space Separator16769.9%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
S88235.8%
 
A2309.3%
 
E1666.7%
 
R1245.0%
 
J1144.6%
 
M1134.6%
 
C1064.3%
 
H843.4%
 
F813.3%
 
L813.3%
 
D712.9%
 
O652.6%
 
P632.6%
 
N592.4%
 
G512.1%
 
B481.9%
 
V441.8%
 
I431.7%
 
T241.0%
 
W70.3%
 
Y30.1%
 
U30.1%
 
Z1< 0.1%
 
K1< 0.1%
 
Ú1< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
o195315.2%
 
e166513.0%
 
r152011.8%
 
a152011.8%
 
i11909.3%
 
ñ8016.2%
 
n7656.0%
 
l7055.5%
 
t5494.3%
 
s3953.1%
 
d3923.0%
 
u3152.5%
 
c2552.0%
 
m2221.7%
 
g1611.3%
 
b1261.0%
 
v890.7%
 
f700.5%
 
p350.3%
 
q240.2%
 
é200.2%
 
y180.1%
 
z170.1%
 
x120.1%
 
h100.1%
 
Other values (7)280.2%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
1676100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin1532290.1%
 
Common16769.9%
 

Most frequent Latin characters

ValueCountFrequency (%) 
o195312.7%
 
e166510.9%
 
r15209.9%
 
a15209.9%
 
i11907.8%
 
S8825.8%
 
ñ8015.2%
 
n7655.0%
 
l7054.6%
 
t5493.6%
 
s3952.6%
 
d3922.6%
 
u3152.1%
 
c2551.7%
 
A2301.5%
 
m2221.4%
 
E1661.1%
 
g1611.1%
 
b1260.8%
 
R1240.8%
 
J1140.7%
 
M1130.7%
 
C1060.7%
 
v890.6%
 
H840.5%
 
Other values (32)8805.7%
 

Most frequent Common characters

ValueCountFrequency (%) 
1676100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII1615695.0%
 
None8425.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
o195312.1%
 
167610.4%
 
e166510.3%
 
r15209.4%
 
a15209.4%
 
i11907.4%
 
S8825.5%
 
n7654.7%
 
l7054.4%
 
t5493.4%
 
s3952.4%
 
d3922.4%
 
u3151.9%
 
c2551.6%
 
A2301.4%
 
m2221.4%
 
E1661.0%
 
g1611.0%
 
b1260.8%
 
R1240.8%
 
J1140.7%
 
M1130.7%
 
C1060.7%
 
v890.6%
 
H840.5%
 
Other values (25)8395.2%
 

Most frequent None characters

ValueCountFrequency (%) 
ñ80195.1%
 
é202.4%
 
á70.8%
 
ú50.6%
 
í30.4%
 
ó30.4%
 
è20.2%
 
Ú10.1%
 

genero
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size12.5 KiB
hombre
513 
mujer
288 
ValueCountFrequency (%) 
hombre51364.0%
 
mujer28836.0%
 
2020-10-09T00:35:52.644026image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-09T00:35:52.784897image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-09T00:35:53.124512image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length6
Mean length5.640449438
Min length5

Overview of Unicode Properties

Unique unicode characters8
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
m80117.7%
 
r80117.7%
 
e80117.7%
 
h51311.4%
 
o51311.4%
 
b51311.4%
 
u2886.4%
 
j2886.4%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter4518100.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
m80117.7%
 
r80117.7%
 
e80117.7%
 
h51311.4%
 
o51311.4%
 
b51311.4%
 
u2886.4%
 
j2886.4%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin4518100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
m80117.7%
 
r80117.7%
 
e80117.7%
 
h51311.4%
 
o51311.4%
 
b51311.4%
 
u2886.4%
 
j2886.4%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII4518100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
m80117.7%
 
r80117.7%
 
e80117.7%
 
h51311.4%
 
o51311.4%
 
b51311.4%
 
u2886.4%
 
j2886.4%
 

edad
Real number (ℝ≥0)

MISSING

Distinct84
Distinct (%)13.1%
Missing160
Missing (%)20.0%
Infinite0
Infinite (%)0.0%
Mean32.68837754
Minimum3.42
Maximum83
Zeros0
Zeros (%)0.0%
Memory size12.5 KiB
2020-10-09T00:35:53.339993image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum3.42
5-th percentile7
Q123
median31
Q341
95-th percentile59
Maximum83
Range79.58
Interquartile range (IQR)18

Descriptive statistics

Standard deviation14.38067179
Coefficient of variation (CV)0.4399322595
Kurtosis0.2502257231
Mean32.68837754
Median Absolute Deviation (MAD)8
Skewness0.4151102474
Sum20953.25
Variance206.8037211
MonotocityNot monotonic
2020-10-09T00:35:53.591544image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
27283.5%
 
25253.1%
 
22253.1%
 
31232.9%
 
21232.9%
 
33222.7%
 
24212.6%
 
28212.6%
 
39202.5%
 
19172.1%
 
32172.1%
 
38172.1%
 
30162.0%
 
29162.0%
 
34162.0%
 
23151.9%
 
36151.9%
 
35151.9%
 
37141.7%
 
43121.5%
 
45121.5%
 
26121.5%
 
20121.5%
 
42121.5%
 
48101.2%
 
Other values (59)20525.6%
 
(Missing)16020.0%
 
ValueCountFrequency (%) 
3.4210.1%
 
3.6710.1%
 
3.7520.2%
 
3.8320.2%
 
470.9%
 
581.0%
 
650.6%
 
770.9%
 
830.4%
 
930.4%
 
ValueCountFrequency (%) 
8310.1%
 
7710.1%
 
7420.2%
 
73.510.1%
 
7320.2%
 
6910.1%
 
6820.2%
 
6720.2%
 
6610.1%
 
6530.4%
 

amigos
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5043695381
Minimum0
Maximum8
Zeros557
Zeros (%)69.5%
Memory size12.5 KiB
2020-10-09T00:35:53.804481image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.089859341
Coefficient of variation (CV)2.160834981
Kurtosis18.35651372
Mean0.5043695381
Median Absolute Deviation (MAD)0
Skewness3.749547323
Sum404
Variance1.187793383
MonotocityNot monotonic
2020-10-09T00:35:53.980565image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%) 
055769.5%
 
117922.3%
 
2253.1%
 
4151.9%
 
3141.7%
 
860.7%
 
550.6%
 
ValueCountFrequency (%) 
055769.5%
 
117922.3%
 
2253.1%
 
3141.7%
 
4151.9%
 
550.6%
 
860.7%
 
ValueCountFrequency (%) 
860.7%
 
550.6%
 
4151.9%
 
3141.7%
 
2253.1%
 
117922.3%
 
055769.5%
 

parientes
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3832709114
Minimum0
Maximum6
Zeros607
Zeros (%)75.8%
Memory size12.5 KiB
2020-10-09T00:35:54.158706image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.8041578127
Coefficient of variation (CV)2.098144651
Kurtosis9.753623974
Mean0.3832709114
Median Absolute Deviation (MAD)0
Skewness2.740037972
Sum307
Variance0.6466697878
MonotocityNot monotonic
2020-10-09T00:35:54.348907image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%) 
060775.8%
 
111013.7%
 
2708.7%
 
350.6%
 
540.5%
 
440.5%
 
610.1%
 
ValueCountFrequency (%) 
060775.8%
 
111013.7%
 
2708.7%
 
350.6%
 
440.5%
 
540.5%
 
610.1%
 
ValueCountFrequency (%) 
610.1%
 
540.5%
 
440.5%
 
350.6%
 
2708.7%
 
111013.7%
 
060775.8%
 

id_ticket
Categorical

HIGH CARDINALITY
UNIFORM

Distinct635
Distinct (%)79.3%
Missing0
Missing (%)0.0%
Memory size12.5 KiB
3823
 
7
EC"4366
 
6
EC0"4565
 
6
53234;7
 
6
5692::
 
6
Other values (630)
770 
ValueCountFrequency (%) 
382370.9%
 
EC"436660.7%
 
EC0"456560.7%
 
53234;760.7%
 
5692::60.7%
 
5692:450.6%
 
Y01E0"882:40.5%
 
U0Q0E0"36:9;40.5%
 
5:487440.5%
 
33598240.5%
 
635540.5%
 
3;;7240.5%
 
488840.5%
 
NKPG40.5%
 
56;;2;30.4%
 
3359:330.4%
 
RE"3997930.4%
 
4638230.4%
 
3964330.4%
 
E0C0"5687330.4%
 
RE"3998230.4%
 
56996430.4%
 
H0E0E0"3574;30.4%
 
4;32830.4%
 
RE"3979430.4%
 
Other values (610)70087.4%
 
2020-10-09T00:35:54.697444image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique529 ?
Unique (%)66.0%
2020-10-09T00:35:55.069701image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length18
Median length6
Mean length6.732833958
Min length3

Overview of Unicode Properties

Unique unicode characters35
Unique unicode categories4 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
567512.5%
 
360711.3%
 
453910.0%
 
94308.0%
 
64197.8%
 
83837.1%
 
23646.7%
 
73596.7%
 
;2965.5%
 
:2504.6%
 
"2113.9%
 
01773.3%
 
E1352.5%
 
1871.6%
 
Q861.6%
 
R851.6%
 
C731.4%
 
U641.2%
 
P350.6%
 
V310.6%
 
Y160.3%
 
S130.2%
 
K100.2%
 
G70.1%
 
H70.1%
 
Other values (10)340.6%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number404074.9%
 
Other Punctuation75714.0%
 
Uppercase Letter57510.7%
 
Lowercase Letter210.4%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
567516.7%
 
360715.0%
 
453913.3%
 
943010.6%
 
641910.4%
 
83839.5%
 
23649.0%
 
73598.9%
 
01774.4%
 
1872.2%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
;29639.1%
 
:25033.0%
 
"21127.9%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
E13523.5%
 
Q8615.0%
 
R8514.8%
 
C7312.7%
 
U6411.1%
 
P356.1%
 
V315.4%
 
Y162.8%
 
S132.3%
 
K101.7%
 
G71.2%
 
H71.2%
 
T61.0%
 
N40.7%
 
J20.3%
 
D10.2%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
c628.6%
 
u523.8%
 
t419.0%
 
k419.0%
 
n14.8%
 
g14.8%
 

Most occurring scripts

ValueCountFrequency (%) 
Common479788.9%
 
Latin59611.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
567514.1%
 
360712.7%
 
453911.2%
 
94309.0%
 
64198.7%
 
83838.0%
 
23647.6%
 
73597.5%
 
;2966.2%
 
:2505.2%
 
"2114.4%
 
01773.7%
 
1871.8%
 

Most frequent Latin characters

ValueCountFrequency (%) 
E13522.7%
 
Q8614.4%
 
R8514.3%
 
C7312.2%
 
U6410.7%
 
P355.9%
 
V315.2%
 
Y162.7%
 
S132.2%
 
K101.7%
 
G71.2%
 
H71.2%
 
c61.0%
 
T61.0%
 
u50.8%
 
t40.7%
 
k40.7%
 
N40.7%
 
J20.3%
 
D10.2%
 
n10.2%
 
g10.2%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII5393100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
567512.5%
 
360711.3%
 
453910.0%
 
94308.0%
 
64197.8%
 
83837.1%
 
23646.7%
 
73596.7%
 
;2965.5%
 
:2504.6%
 
"2113.9%
 
01773.3%
 
E1352.5%
 
1871.6%
 
Q861.6%
 
R851.6%
 
C731.4%
 
U641.2%
 
P350.6%
 
V310.6%
 
Y160.3%
 
S130.2%
 
K100.2%
 
G70.1%
 
H70.1%
 
Other values (10)340.6%
 

precio_ticket
Real number (ℝ≥0)

Distinct20
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.453183521
Minimum1
Maximum50
Zeros0
Zeros (%)0.0%
Memory size12.5 KiB
2020-10-09T00:35:55.280505image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile11
Maximum50
Range49
Interquartile range (IQR)2

Descriptive statistics

Standard deviation4.629591556
Coefficient of variation (CV)1.340673477
Kurtosis31.3220353
Mean3.453183521
Median Absolute Deviation (MAD)1
Skewness4.592786914
Sum2766
Variance21.43311798
MonotocityNot monotonic
2020-10-09T00:35:55.543215image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%) 
131339.1%
 
216520.6%
 
312816.0%
 
4445.5%
 
6344.2%
 
8253.1%
 
7162.0%
 
5151.9%
 
9141.7%
 
1570.9%
 
1270.9%
 
1170.9%
 
1460.7%
 
2660.7%
 
2140.5%
 
2330.4%
 
5020.2%
 
2520.2%
 
1020.2%
 
1710.1%
 
ValueCountFrequency (%) 
131339.1%
 
216520.6%
 
312816.0%
 
4445.5%
 
5151.9%
 
6344.2%
 
7162.0%
 
8253.1%
 
9141.7%
 
1020.2%
 
ValueCountFrequency (%) 
5020.2%
 
2660.7%
 
2520.2%
 
2330.4%
 
2140.5%
 
1710.1%
 
1570.9%
 
1460.7%
 
1270.9%
 
1170.9%
 

fila
Categorical

MISSING

Distinct2
Distinct (%)1.1%
Missing624
Missing (%)77.9%
Memory size12.5 KiB
adelante
164 
medio
 
13
ValueCountFrequency (%) 
adelante16420.5%
 
medio131.6%
 
(Missing)62477.9%
 
2020-10-09T00:35:55.789160image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-09T00:35:55.945012image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-09T00:35:56.097348image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length8
Median length3
Mean length4.056179775
Min length3

Overview of Unicode Properties

Unique unicode characters9
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
n141243.5%
 
a95229.3%
 
e34110.5%
 
d1775.4%
 
l1645.0%
 
t1645.0%
 
m130.4%
 
i130.4%
 
o130.4%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter3249100.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n141243.5%
 
a95229.3%
 
e34110.5%
 
d1775.4%
 
l1645.0%
 
t1645.0%
 
m130.4%
 
i130.4%
 
o130.4%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin3249100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n141243.5%
 
a95229.3%
 
e34110.5%
 
d1775.4%
 
l1645.0%
 
t1645.0%
 
m130.4%
 
i130.4%
 
o130.4%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII3249100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
n141243.5%
 
a95229.3%
 
e34110.5%
 
d1775.4%
 
l1645.0%
 
t1645.0%
 
m130.4%
 
i130.4%
 
o130.4%
 

nombre_sede
Categorical

Distinct3
Distinct (%)0.4%
Missing2
Missing (%)0.2%
Memory size12.5 KiB
fiumark_palermo
579 
fiumark_chacarita
149 
fiumark_quilmes
71 
ValueCountFrequency (%) 
fiumark_palermo57972.3%
 
fiumark_chacarita14918.6%
 
fiumark_quilmes718.9%
 
(Missing)20.2%
 
2020-10-09T00:35:56.299843image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-09T00:35:56.431339image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-09T00:35:56.598527image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length17
Median length15
Mean length15.34207241
Min length3

Overview of Unicode Properties

Unique unicode characters18
Unique unicode categories2 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
a182714.9%
 
r152712.4%
 
m144911.8%
 
i10198.3%
 
u8707.1%
 
f7996.5%
 
k7996.5%
 
_7996.5%
 
l6505.3%
 
e6505.3%
 
p5794.7%
 
o5794.7%
 
c2982.4%
 
h1491.2%
 
t1491.2%
 
q710.6%
 
s710.6%
 
n4< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter1149093.5%
 
Connector Punctuation7996.5%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
a182715.9%
 
r152713.3%
 
m144912.6%
 
i10198.9%
 
u8707.6%
 
f7997.0%
 
k7997.0%
 
l6505.7%
 
e6505.7%
 
p5795.0%
 
o5795.0%
 
c2982.6%
 
h1491.3%
 
t1491.3%
 
q710.6%
 
s710.6%
 
n4< 0.1%
 

Most frequent Connector Punctuation characters

ValueCountFrequency (%) 
_799100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin1149093.5%
 
Common7996.5%
 

Most frequent Latin characters

ValueCountFrequency (%) 
a182715.9%
 
r152713.3%
 
m144912.6%
 
i10198.9%
 
u8707.6%
 
f7997.0%
 
k7997.0%
 
l6505.7%
 
e6505.7%
 
p5795.0%
 
o5795.0%
 
c2982.6%
 
h1491.3%
 
t1491.3%
 
q710.6%
 
s710.6%
 
n4< 0.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
_799100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII12289100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
a182714.9%
 
r152712.4%
 
m144911.8%
 
i10198.3%
 
u8707.1%
 
f7996.5%
 
k7996.5%
 
_7996.5%
 
l6505.3%
 
e6505.3%
 
p5794.7%
 
o5794.7%
 
c2982.4%
 
h1491.2%
 
t1491.2%
 
q710.6%
 
s710.6%
 
n4< 0.1%
 

Interactions

2020-10-09T00:35:36.262416image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-09T00:35:36.499671image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-09T00:35:36.687729image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-09T00:35:36.871031image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-09T00:35:37.086037image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-09T00:35:37.277897image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-09T00:35:37.474322image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-09T00:35:37.671532image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-09T00:35:37.860218image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-09T00:35:38.048073image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-09T00:35:38.433845image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-09T00:35:38.647454image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-09T00:35:38.841513image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-09T00:35:39.040328image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-09T00:35:39.233198image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-09T00:35:39.426878image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-09T00:35:39.616435image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-09T00:35:39.804883image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-09T00:35:40.011172image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-09T00:35:40.199335image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-09T00:35:40.390370image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-09T00:35:40.586876image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-09T00:35:40.810316image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-09T00:35:41.030760image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-09T00:35:41.242045image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-09T00:35:41.477850image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-09T00:35:41.708789image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-09T00:35:41.942856image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-09T00:35:42.170358image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-09T00:35:42.368509image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-09T00:35:42.570273image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-09T00:35:42.780643image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-09T00:35:43.015110image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-09T00:35:43.225042image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-09T00:35:43.512076image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-09T00:35:43.759166image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-09T00:35:44.012009image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-09T00:35:44.239190image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-09T00:35:44.443540image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-09T00:35:44.732466image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-09T00:35:44.966960image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-09T00:35:45.203104image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-09T00:35:45.582205image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-09T00:35:45.846939image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-09T00:35:46.100968image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-09T00:35:46.360988image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-09T00:35:46.640796image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-09T00:35:46.899833image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-09T00:35:47.193768image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2020-10-09T00:35:56.776502image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-10-09T00:35:57.052470image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-10-09T00:35:57.329908image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-10-09T00:35:57.613365image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-10-09T00:35:57.890201image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-10-09T00:35:47.754106image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-09T00:35:48.404456image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-09T00:35:48.743497image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-09T00:35:48.942744image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Sample

First rows

unnamed:_0id_usuariovolveriaUnnamed: 0tipo_de_salanombregeneroedadamigosparientesid_ticketprecio_ticketfilanombre_sede
011611701164dSeñor Camilo Pedrohombre73.50059258;1NaNfiumark_quilmes
165765806574dSeñora Raquel Angelicamujer35.011586:6;2NaNfiumark_quilmes
27937940793normalSeñor Antonio FedericohombreNaN00RE"398223NaNfiumark_chacarita
345445504544dSeñor Osvaldo AurelianohombreNaN00C17"4:391NaNfiumark_palermo
417217311724dSeñorita Rita Eudosiamujer4.0115699642NaNfiumark_palermo
53513520351normalSeñor Raimundo LonginohombreNaN003357324adelantefiumark_palermo
617617701764dSeñor Celestino WenceslaohombreNaN3163553NaNfiumark_palermo
790910904dSeñor Carlos Gregoriohombre32.0005654981NaNfiumark_palermo
82242251224normalSeñor Carlos Roberto Franciscohombre41.0103;;659adelantefiumark_palermo
98798801879normalSeñora Lidia Barbaritamujer59.001339899adelantefiumark_chacarita

Last rows

unnamed:_0id_usuariovolveriaUnnamed: 0tipo_de_salanombregeneroedadamigosparientesid_ticketprecio_ticketfilanombre_sede
79181181208114dSeñor Raúl Eduardohombre42.000C16"6::933NaNfiumark_palermo
79259059105904dSeñor Juan Mateo Fernandohombre38.000UVQP1Q"40"53234951NaNfiumark_palermo
79354754815473dSeñor Francisco AnicetohombreNaN00UE1RCTKU"43682NaNfiumark_chacarita
79448490484dSeñor Armando NelsonhombreNaN2048843NaNfiumark_chacarita
79567267306723dSeñor Lucio Fernandohombre73.000E0C0"467:22NaNfiumark_palermo
7964474481447normalSeñor Bernardo Melesiohombre37.0003359;63NaNfiumark_palermo
79763563616353dSeñorita Lidia Catalinamujer31.00045988:2NaNfiumark_palermo
79884784808474dSeñor Arturo Antenorhombre38.00056;4351NaNfiumark_chacarita
79930330413033dSeñorita Natividad SofiamujerNaN004487;52adelantefiumark_quilmes
80038438503844dSeñor Isidoro SebastianhombreNaN0056;4491NaNfiumark_palermo